---
title: "Sleep Dashboard "
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    theme: 
      bg: "#101010"
      fg: "#FDF7F7" 
      primary: "#ED79F9"
    logo: logo.png
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(lubridate)
library(reshape2)
library(ggplot2)
library(ggpubr)
library(plotly)
library(GGally)
setwd("~/Desktop/module_3/all_data")

Lorena <- read_csv("Lorena_converted (3).csv")
Lorena$ID<-"W1"
Martin <- read_csv2("Martin.csv")
Martin$ID<-"M1"
Nely <- read_csv2("Nely.csv")
Nely$ID<-"W2"
dd<-merge(Nely, Martin, all = TRUE)
dd<-merge(dd, Lorena, all = TRUE)

```

Overview
=======================================================================

Row
-----------------------------------------------------------------------

###  Global sleep overview

```{r}
dd1<-dd[,c(1,11,12,13,14,98)]
dd1<-as.data.frame(dd1)
dd1<-melt(dd1, id=c("calendar_date","ID"))
p<-ggplot(dd1, aes(x=calendar_date,y=round(value*100),group=variable, fill=variable)) +
  geom_density(stat = "identity",alpha=.6)+theme_ipsum() +scale_fill_ipsum()+
  labs(x = "Date",
       y = "Sleep in %",
       color = "Sleep variable")+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
> something here

Row
-----------------------------------------------------------------------

### total sleep overview

```{r }
p<-ggplot(dd, aes(x = calendar_date, y = total_sleep_hours)) +
  geom_line()+geom_point()+geom_smooth()+theme_ipsum()+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```








Corr_Night analysis 
=======================================================================

Row 
-----------------------------------------------------------------------

### **Influence from Median_hr_sleep on sleep quality**

```{r }

p<-  ggplot(dd,aes(x=perceived_sleep_quality, y=median_hr_sleep, fill=factor(perceived_sleep_quality))) +
  geom_boxplot() +geom_point()+
  scale_fill_ipsum()+
  labs(x = "perceived_sleep_quality",
       y = "median_hr_sleep",
       color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))

```

### **Influence from median_stress_sleep on sleep quality**

```{r}

p<-  ggplot(dd,aes(x=perceived_sleep_quality, y=median_stress_sleep, fill=factor(perceived_sleep_quality))) +
  geom_boxplot() +geom_point()+
  scale_fill_ipsum()+
  labs(x = "perceived_sleep_quality",
       y = "median_stress_sleep",
       color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))

```



Row 
-----------------------------------------------------------------------


### **Correlation  stress & HR**

```{r}

p<-ggscatter(dd, x = "median_stress_sleep", y = "median_hr_sleep", color="ID",
          add = "reg.line", conf.int = FALSE, 
          cor.coef = FALSE, cor.method = "pearson")+
  stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```

> The p-value of the test is less than the significance level alpha = 0.05. We can conclude that hr sleep and stress sleep are significantly correlated with a correlation coefficient beetween 0.64--0.88.

### **Correlation vigorous_activity_hours & total_sleep_hours**

```{r}

p<-ggscatter(dd, x = "vigorous_activity_hours", y = "total_sleep_hours", color="ID",
          add = "reg.line", conf.int = FALSE, 
          cor.coef = FALSE, cor.method = "pearson")+
  stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```

>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .

### **Correlation steps & total_sleep_hours**

```{r}

p<-ggscatter(dd, x = "steps", y = "total_sleep_hours", color="ID",
          add = "reg.line", conf.int = FALSE, 
          cor.coef = FALSE, cor.method = "pearson")+
  stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```

>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .




Global corr analysis 
=======================================================================


Row {}
-----------------------------------------------------------------------

```{r}
dd2<-dd[,c(6,42,59,69,69,94:98)]
ggpairs(dd2)
```

Row {}
-----------------------------------------------------------------------

```{r}
dd2<-dd[,c(6,12,27,94:96)]
ggpairs(dd2)
```